WARNING, currently using machine_learning_functions_test_par.py

This code compares two regulations methods for different pathways. Each pathway is in a folder. The functions utilized are in Basic_Functions Equilibrator parses reactions from .dat folders machine_learning_functions contains all functions to run a reinforcement learning program. max_entropy_functions contains all functions to run a maximume entropy regulation program.

To run: Place all the code folders into a new folder, "new_folder". cd into new_folder inside python or make it your current working directory. Open one of the python files for a pathway, e.g. "GLUCONEOGENSIS" Using "new_folder" as your cwd you can run the python file.

To run with slurm: cd into one of the pathway folders type "sbatch SBATCH.sh" the script runs multiple jobs for each n value. The simulation id's are in array. To run one, use --array=1, to run more use --array=1,2,...k, to run simulations 1-k for a specific n value.

Necessary Arguments: 1: simulation number 2: n value

Additional Arguments: 3: use metabolite data (0 uses rule of thumb, 1 uses data) default 0 4: learning rate (1e-6, 1e-7, 1e-8) default 1e-8 5: epsilon greedy init (0-1) default 0.5 6: epsilon threshold (episodes before eps=eps/2) default 25 7: gamma (0-1) default 0.9

To run a short simulation, cd to the GLYCOLYSIS_TCA_GOGAT folder. Use the command in one of the batch scripts: python ./GLYCOLYSIS_TCA_GOGAT_FUNCTION 1 4 0 1e-06 0.05 2

In Cascade, run "/home/scicons/cascade/apps/python/3.6/bin/python ./GLYCOLYSIS_TCA_GOGAT_FUNCTION.py 1 2"